Flowshop scheduling with learning effect and job rejection

Baruch Mor*, Gur Mosheiov, Dana Shapira

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


We study scheduling problems on a proportionate flowshop. Three objective functions are considered: minimum makespan, minimum total completion time, and minimum total load. We consider a learning process; thus, the processing time of a job processed later in sequence is reduced. The scheduler has the option of job rejection; i.e., only a subset of the jobs are processed and the rejected jobs are penalized. An upper bound on the total permitted rejection cost is assumed. Since the single-machine versions of these problems were shown to be NP-hard, we focus on the introduction of pseudopolynomial dynamic programming algorithms, indicating that the problems are NP-hard in the ordinary sense. We provide an extensive numerical study verifying that the proposed solution algorithms are very efficient and instances containing up to 80 jobs are solved in no more than 5 ms.

Original languageAmerican English
Pages (from-to)631-641
Number of pages11
JournalJournal of Scheduling
Issue number6
StatePublished - Dec 2020

Bibliographical note

Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.


  • Dynamic programming
  • Job rejection
  • Learning effect
  • Proportionate flowshop
  • Scheduling


Dive into the research topics of 'Flowshop scheduling with learning effect and job rejection'. Together they form a unique fingerprint.

Cite this